{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pyplot 教程\n",
    "\n",
    "pyplot界面介绍。\n",
    "\n",
    "## 一、pyplot简介\n",
    "\n",
    "`matplotlib.pyplot`是使`matplotlib`像`MATLAB`一样工作的命令样式函数的集合。 每个`pyplot`函数都会对图形进行一些更改：例如，创建图形，在图形中创建绘图区域，在绘图区域中绘制一些线条，用标签装饰绘图等。\n",
    "\n",
    "在`matplotlib.pyplot`中，跨函数调用保留各种状态，以便跟踪当前图形和绘图区域之类的东西，并将绘图函数定向到当前轴（请注意，此处和大多数位置的“轴” 文档是指图形的轴部分，而不是多个轴的严格数学术语。\n",
    "\n",
    "生成可视化的`pyplot`非常迅速，只给y轴的数值，系统会假设这是序列数据，自动生成x轴的数据为[0, 1, 2, 3]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([1, 2, 3, 4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fef62c8c898>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于每对x，y参数，都有一个可选的第三个参数，它是表示图的颜色和线条类型的格式字符串。 格式字符串的字母和符号来自MATLAB，您将颜色字符串与线型字符串连接在一起。 默认格式字符串是“ b-”，这是一条蓝色实线。 例如，要用红色圆圈绘制以上内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.axis([0, 6, 0, 20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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heP+//3u46KLJ+5csKdp3223wl39ZbJszpwj82bPhS18qru+ZZ4prGNv38pfDfvuNv8/xxxe/ro2dN2cOvOxl4/svuKAI7ur3rt5/zTXFf8/qbS1mt4ymtLP7Y+l5cNzXYeiDcMvle+7+WHoeHHNVEdrb9oV7/hRuubyj3R89OzY8E0ZHJ4bbnDkwf36x7447Jleur389HHdccd6Xvzw5HN/5Tli6FJ56Cv7szybvP/98OPts+Pd/h7e+deL+HTuKQF6+HIaG4Hd+Z3Kbr7sO3vte+MlP4O1vn7x/5Uo4/XS49VZ43/vGg28sIL/xDRgYKM7/4hfHt48dc9FFcNhhcM898MMfTtw3e3bx3i9/OTz8cFElV++bPbuonmfNKv77QFHl7+4vSJfbXbeM4d4HdobVgcNw5llww/Xw7MI9B/WBw3DBa2GfrfDSfnDZRnh2Ye0QfVnV8WMq5+WWyVV/u8K9rrDevr3oG4XiV+wtWyYG3D77wLHHFvt//OPiDar3v+pVRbgBXHpp0a9aHb5HHw2f/nSxf9myovug+vx3vasIOICDDioqyGrvfz9cfXXx8957F4Fb7aMfhcsugxdeGO//ra4wP/EJ+NSninA/8cTxcBz7fu658J73wMgI/PmfTw7XpUuL6/+P/4B//MeJwTx7dlF1L1wIzz8PmzZNDt/Zs2Evx3G0in3uJVNvWO8MtpMG4bA1cOIg3HL5nvupTxqEqIRHbN953rSOHzN2Hrs5r5ZNm8bDcSz8OKP28Z/8ZPGr7he/WLz+2tfgRz9i80DV+XPmFL+SA3zoQ7Do5omV66GHFlUfFCE9duyYN74R7r23+PmSS2Dt2on7jz9+PNxvvhl+8YuJATd37vixc+cW4VwdjscdN77/4ouL66ne/4Y3jO+/9VbYd9+JwXnwwcW+OXPgN78ptk1Vnb7ylePXMZX584t+51rmzYM//uPa+/ffH37rt2rv14yzcm+hRn6db7iqhkldJrCb0R+1qvBfDU/uFz3ySOKwrbWr8Ae2FSG4bl3xa3jl/Nh7CBatn9yA4TeTPx0df//Vq+GII/ZchX/60zA4cWjkQoZ5nMm/BSxgM5tnLy5C7emni4u+5JIiYKvDce5cuOmm4qS//dvi1/vq8J03Dz7+8WL/rbcWfwjV4XnQQfB7v1fs37ixGCFR/f5z5kzsm5VmUNu7ZSJiCXAZsDdwZWZ+fnfH1xvujQRiO87ZGVa7HA+QW6vCc9994RWvgO3biVmVLoCpgvrZ52DFikn9ovGFS2uH9dtOLMLp938f7rsPTjuNeOiXtfvCmSJhb76Z+MYPx48fUzkvL/yvxR3+73yn6CaoBF8M/6rmn1m+58zxgPzsZ2HRIhbOG+Xxp/addOyCV+1g8+N7FVXvL385uWvgqKOKLonnnhuvfHu871RqRFvDPSL2Bn4BvAPYBNwF/FFm3l/rnHrDva7K9de/huefJ169qPY5N9w4sXJdtAiWLZve55x+etE3++KLxD131z6+OkTPP7+42bV1K7HfnNpBPfJk8etxtdmziRe31g7r//x2+NjHxvtzL7mEuOnztavwv7p5cngODBCfWVK7Cv/KXcVNqV10zY1IqU+0O9z/E/A/M/O/VF5fDJCZn6t1TkPhXisQDziwGAr1s58VB59wAtxxB0HWPmfX6vXkk2HVqundVFy2rLh5NHs28YPv1z7+s/97PDzf9CZ429sgk9gragf19h3Fjaux8K1Up3XfuDztvNpV+A+m7gs3qKXu1+4bqocAj1a93gT87hSNWg4sBzjssMPq/5RaN/s++EF49avHj7vwwmJo14d2c8769RMr1wMO2PPnjFm5suqidnP8pz41+Roiin8M3rxiPHhnjcIxK2D1X8BeCydX7ru2aed71b5xOWvxWrZNMZ581hG1x5Mb4FJvm4nK/UxgSWb+aeX12cDvZuYUTxwU6q7c66xc23VOQ5/RQFW9z0eOYdv8yV0ms0bezEtfWTflOZLKp92V+2PAa6peH1rZ1jqNDLlrwzn7LRnkhSmO329J7c9opKo2wCXtyUyE+13AkRFxBEWonwW8t5Uf0EggtuOco05dy/rNk48/6lSDWlJ7zdRQyHcDf0MxFPLqzPzs7o4vyzh3SWqntj+hmpm3ALfMxHtLkvbMSR4kqYQMd0kqIcNdkkrIcJekEuqKWSEjYgR4uMHTDwaebGFzek0/X38/Xzv09/V77YXDM3OKx9i7JNybERFDtYYC9YN+vv5+vnbo7+v32vd87XbLSFIJGe6SVEJlCPfdrAXWF/r5+vv52qG/r99r34Oe73OXJE1WhspdkrQLw12SSqinwz0ilkTEv0XEhoi4qNPtaaeIuDoinoiIezvdlnaLiNdExO0RcX9E3BcRF3S6Te0SEXMi4l8i4meVa/9fnW5Tu0XE3hGxLiK+3+m2tFtEPBQR/xoR6yNit1Pp9myfeyMLcZdJRJwIPAv8XWYe3en2tFNELAIWZeY9EfEy4G7gjH74s4+IAA7IzGcjYh9gDXBBZv5zh5vWNhFxITAAvDwzT+t0e9opIh4CBjJzjw9w9XLlfjywITM3ZuYo8G1gWYfb1DaZuRp4qtPt6ITMHM7Meyo/bwEeoFi7t/Sy8Gzl5T6Vr96s0BoQEYcCS4ErO92WbtfL4T7VQtx98T+4xkXEYuAY4M7OtqR9Kt0S64EngNsys2+unWIRoE8AO/Z0YEkl8H8j4u6IWL67A3s53NXnIuJA4EbgY5n5m063p10yc3tmvplifeLjI6IvuuUi4jTgicy8u9Nt6aC3ZuaxwLuAD1e6Z6fUy+E+8wtxq2tV+ptvBK7LzJs63Z5OyMxngNuBJZ1uS5ucAJxe6Xf+NnByRHyzs01qr8x8rPL9CeC7FN3TU+rlcN+5EHdE7EuxEPf3OtwmtUHlpuJVwAOZ+aVOt6edImJ+RMyt/LwfxYCC/9fZVrVHZl6cmYdm5mKK/99/nJnv63Cz2iYiDqgMICAiDgDeCdQcLdez4Z6Z24CPAD+kuKH2D5l5X2db1T4R8S1gLXBURGyKiHM73aY2OgE4m6JyW1/5enenG9Umi4DbI+LnFAXObZnZd0MC+9QCYE1E/Az4F+AHmXlrrYN7diikJKm2nq3cJUm1Ge6SVEKGuySVkOEuSSVkuEtSCRnuklRChrskldD/B7qkYcqiEGHmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "# red dashes, blue squares and green triangles\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 二、 用关键字字符串绘图\n",
    "\n",
    "在某些情况下，某种格式的数据，该格式允许使用字符串访问特定变量。 \n",
    "\n",
    "例如，使用numpy.recarray或pandas.DataFrame。\n",
    "\n",
    "Matplotlib允许您为此类对象提供data关键字参数。 如果提供的话，可以使用与这些变量相对应的字符串生成图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4+2Y5yvarqrufSHQD0PpVt2oj26u+R2H+abjE3+b7Hti7hL+ePL3d8ZxWfiiPr59HYyy1DmlV8IuXKWWH0L94HA2hd3GcGuL/Pk158bjLKSm6vt0xZUvW7xxExC0iH4nIrMTj4SLygYisEpF/iogv2zEak02762exsfIiGkPvoYRQrU9cUdeh2khN/WOs2zaVSHRTtkNtF1WHqtoZKMHU9kepa5id1pgOLR5I/0BR63EoROs9NG4opP6zYnZ+VsAVDzzD8TNm8c7Wm/F6JiL4ESnEJb0Q/BQGpjK0bDZuV+vvnUuynhyAG4BlTR7fAvxZVUcCVcA1WYnKmBwQDC9mW9V3UG3tCzRCzNnJhooLUe06ZT9DkSU4SeYEtES1npr6x9MYUXzdox8echZ+V/JGFSciNKwtIripkFiDBxBQcFTZ0dDAH95ey9mPncySmhmUl/yZASW3ceAB8xhYeh9ud0laY+9sWU0OIjIIOAu4J/FYgCnAk4ldZgLtvzc0ppvYWXMrqqEU9owRc3ZR1/hy2mPqLDGnOqURPnu/pn0jl/bHcf1H8ZPDzmmWIJyo0LiuCA27QAWS9E4Eo1FCsRg/f3UZ/1peTq/8aXjcpWmPOR2yfedwG/ADvmig6wtUq+qeqYmbgIHZCMyYbIvGdlAffJ19O2lbolrPrtq/pjWmzuSSQlI9ty9eU5CeYPYxffB4/u+Yq5hQMgyfy0Oe20tocyEaS54U9tUYjfL7N99myfaK9AebJllLDiJyNlChqgv38/XXisgCEVlQWVnZydEZk32hyKe0t8stFF6apmg6n983tl37i+RTmH92mqJpbnzJUO499hqePvk7fG3QybjCPlJJDHuEYzH+Pn9++gJMs2yOVjoOOFdEzgQCQBFwO1AsIp7E3cMgYHOyF6vqDGAGxBfey0zIxmTO/kyWUlpfDyhXrN+wg/kL1lI2aCr9B8wGaT3uql2FfPD+ITjhMgYeMI/Tpx5KcXF+RmIdlN+HDZsb21372VHl5ZWrqAkG6R0IpCm69MlaclDVHwM/BhCRk4GbVPUKEXkC+DLwGHAl8Gy2YjQmm9yu/mizIZGty/UJVo2NYX75m2dYtHgjjiqFvQbwk597KSiI4XI1//JVhWf+dSxzXjkS8BCNLsHn+4x77n+Tb1x9EhdfODEjcb+1fj2x/VhXw+d28/HWrZw0PLMlPjtDtvsckvkh8D0RWUW8D+LeLMdjTFYEfIfjcqVWUCasLiqjvajUMSzbdTe14XXpDW4//fJ/n+GTRRsIhaNEIjGqdgX4w80XUVubRyTcfILa63PG8dqccUSjHqKJm4tw4rX3zXyTN976LCNxN0b2bxSYAnWhrrFcxr5yYhKcqr4OvJ74fQ0wKZvxmPTYUF3N00uXsWX3bvrk5XHWmDEcNqAs22HlLBEXJYXfYsfuW1BNvtqno7Ap2ps69ce7dkNr2R76O59V30+J/zCOHvAH/O7ijMbdkvUbdvDJoo2EI3svjFexvQ+//OnXOP7EFVz45aWIaweCm5ijvDD7eMLh5DOKQ6Eo9/7jTU46YUzaYw94PNSQyqixvQlQ4OuaU7VyIjmY7q0+HObGWS/wzvr1OKpEHAcX8NBHHzOsTx9mnD+dA4p6ZTvMnNSn11XUNj5LKLwU3efLSRXWRfsQVA/apKNUiaEaY2fwE97YfBVTBj2Kx5X5MpP7WvDhOloqLhYM+pnzymEMKruOr1w+DtUw69eHiEYfA1q+at+2vYaq6nr6FKd3FNOEgQN5ccVKnHY2LYViMQ4t65+mqNIrpWYlEfGJyOEicpjNWDbtEY7F+MrjT/D2+vWEYjEiTrwN3SE+3G/Fjh1c8PDD7Gzo2msEpYuIj8Glj5PnPxqRPOJLZsTtdvwE1Yu28N9YidAQ3cba3U8mfT7TUvledRxwu3rhcfclFnO1WfTGJUI02r5+mf3xjQkT8HvatyaSACcMG0q/gswMv+1sbSYHETkLWA38BbgTWCUi09IdmOkeZi//jJU7dxJuYY39mCrVjUHuev+DDEfWdbhcBQzu/xhD+j9Dr/zzcLv645Le7HT67HXHkIyjIVZWP9jiFXsmjR83FJer5Xj9fi8Txn/RcTtkcN82Rwjl5fko6ZP+L9/DywcwqKg3rnZUaPN7PFw7MTMd5umQyp3Dn4BTVPVkVT0JOAX4c3rDMt3FjHnzaYy0Pkwx4jg8vvhTQtGuMQwzWwK+wzig752MHPgxowYtI0RqhedDsV3EWl1+IzMOHF7KQWPK8Sa5Ane7XQwa2IdDD/lizmsg4OXMMw7H70ve+u33e7jkokm43ZkZV/N/08+lwOdNaaZDnsfDVeOPZMKgrjuHN5W/1VpVXdXk8RqgNk3xmG5EVVm1K7XlDkRgU83uNEfUvUjKE7K0Hfum1//+6kLGjB6A3+/B5RJE4klg+LB+/OH/XdKsGem6a07m4IMPIC+wdyIMBLwcPXEEF12QuSvzYX368OTll9E3P598b/LE7HW58Hs8fHPiBG464fiMxZYOLXZIi8gFiV8XiMgLwOPER2ZdBHTdaX/GdBPF/rHsDH7U5n75ngNwu3JjElZhgZ87/vwVPluxlXkL1qKqjB83lEPGDkzav+Dzefjj7y5h3vw1PP3ch1Tu2M0B5X24YPpRjB83tM0+ic42sm9f3rz2G8z+bAV3z5vHhuoavC7X57NRvnzIWK48ajzD+/TJaFzp0NpopXOa/L4dOCnxeyXxGc3GtEpEGFFSwsqdO9vcVxUG9e46yxnngtHFVzFv+3JiLQxzBXBLgFHFV2YwqtSMGV3OmNHlKe3rdruYfMxIJh8zMs1Rpcbv8XDBIWO54JCx7KhvoCYYJODx0K8gH7+n+wwAbfFMVPXqTAZiuqdrJ03kF6++2mq/g9fl4qLDDu1W/7EyYUD+8fTPO5qKxveT9im4xE+RbxTDis7LQnQ9Q7+CfPoVZGYZj0zLxRnSphs5+6AxjOrbF587+TBAtwjFeQGuP+boDEfW9Ym4OHrAHxjW63xc4scj+Qge3JKHS3wMLDiVEw+YgUtS67g2pinJhSFuHTVhwgRdsGBBtsMwLagPh7lh1mzeXb9hr0lwfo/HJsF1kohTx5b61wjFqvC5elFecDJ+d9dv9zbJqSqrPlpLxYYdeP1exkwcQe9+7W+WFZGFqjoh6XNtJQcRcatq8kHqOcKSQ9ewobqap5csZUttrS2fYcx+UFVeeeANHvz1E1RX1OByuxFRwsEox5w9nmt+dwUDR6bWlwMdTw5rgKeA+1U1JxeLt+RgjOnuVJU7/uteXv7H64Qamq/zJC4hrzDAn177H0YemdoqsK0lh1T6HI4AVgD3iMj7iSI7NqzEGGMy6OWZr7eYGADUURp2N/LD039NsIV92qPN5KCqtar6d1U9lvhy2r8EtorITBHJjbFlxhjTjakqD/3myRYTQ1ORUJTX//luh4+ZytpKbhE5V0SeJl7z+U/AgcDzwAsdjsAYY0yrVn64hurK1FYQaKwL8q/bZ3f4mKkMLF8JvAb8QVWbpqMnReTEDkdgjDE9SG04RNRx6O0PpLyQ3/b1O1pdtHBfOza3PfG0La0mBxFxA/9Q1V8ne15Vv7O/BxaRAPAm4E/E8aSq/lJEhhMvEdoXWAh8VfenmK4xxuQIVWXWuuXcsehd1tTswiVCvtfL1QdN4JuHTCTf23olBJ/f067VsTzejk8obbVZKTGE9ewOHyW5EDBFVY8AxgFniMgxwC3An1V1JFAFXJOm45scoqps27mb2obsrx66v1RjaOPTOLW3oY3Popr+OgOma/jVvFf5wbsvsqJ6B1F1CDsxqkNB7lr8HtNfeJC6SOt9CWMmjSQSTm3VYpdLOPzEsR2OOZXRSu+IyJ0icoKIjN/zp6MH1ri6xENv4o8CU4A91UlmAtM7eiyT20KRKN+45Z9c+LP7+dL37uaZNxdnO6R2U3XQqm+iNb+C+rvQml+gVdflRB0Fk12vb17DE6sW0xhtXtEu5MRYt7uK38yf2+p7FJf2ZuK0I5EUmpa8AR8X3XTufse7RyrJYRxwCPBr4p3RfwL+2OEj83ln98dABfAK8aJC1aq6J0VuApIuiJ4YUrtARBZUVlZ2RjgmS55/ZwnL11cQisQIR2Pc8shcgqH9K+ieNeG3IPIhsGcRvEaIzIfwO9mMyuSAvy1+n4YkiWGPsBPj2TVL27x7+MbvriBQ4G91H3+ej4lnjGPMhBH7FWtTqTRMXaOqa5puEJEDO3xkPm+2GicixcDTwEHteO0MYAbEJ8F1RjwmO+oaQkSbVIpTVYKRKAF/F1oTKLY9eR3MWEXmY+mA9buruP/TD5m7YTURx2FEcQnfOGwiJw4a1q4qaOYLH1VuaXMfr8vNkp0VHD1gcIv7DBp9AH+c+yt+ePpviEZiBOu+aIJ1uQRvIJ4YfvLIDZ0SdyrJ4Ulg32akJ4CjOiUCQFWrReQ1YDJQLCKexN3DIGBzZx3H5KbTJo7h/hfnEY7E8LhdTDhoMMWFedkOq328BxNvFW1CHfCmfL2TdXcvmsetC97+fP0rgK31tXy0fQuj+vTlgTMvpsjX+pWr2X+67+cnidFHjeCRDf/H64+9w79un82OzbvweD0ccfIhXPTf5zBmYudNPWut2M9BxJuTejcp/ANQRCfUcxCRUiCSSAx5wGnEO6NfA75MfMTSlcCzHT2WyW0DS3vz2K++xusfraK4MI/TJo7JdkjtJt7D0MLroe5OEB9oGAq/g3g73jGYCf9asYQ/L3iHUJJa3/XRCEt3VnL1S0/x5DmXZbzATld3SEkZH+1o/e4h7MQ4uKR/Su+XVxBg2jVTmXbN1M4Ir0Wt3TmMIT5SqZi9C//UAt/shGOXAzMTw2VdwOOqOktElgKPicj/Ah8B93bCsUyOK+9bxGWndnicQ1a5Cq9DA2dCbCO4hyCeQdkOKSWOKr+b9wbBWMujYcJOjGU7K/iwYgtHlXXdusjZ8K3DjuHGt55vsd/B63Ixbchoevtyq4Zaa8V+ngWeFZHJqvpeZx9YVRcBRybZvgaY1NnHMyYTxDMYPC23G+eieds20RBteypRYzTC/Z9+aMmhnU4bPJIzhozmxfUraIztnSC8LjcD8gv51dGnZim6lqXS57BKRH4CDGu6v6p+PV1BGWMyZ1NtTdK+9H0psLZmV9rj6W5EhD8dfxZH9R/IXYvfZ0ewAXeiae6SUYdz47jjc+6uAVJLDs8CbwGvAjld18GkR0VwJatr38Elbkb1OokS/xCiMYc5y1bx1IdLqA+FOWHUMC6ZeDh9CrpYR7Ih4PakPBIpz5N7I8hUlWAsir8d55FpIsIVY47k8tHj2NZQS8RxKMsvxO/O3dK4qUSWr6o/THskJueoKnO338bymleJaQQQFux8jMOKvsz/PZ/HZ9t30BCO3yYv2bKd+95ZyEPXXMzoAf2yG7hpl2MHDiHitH3dl+/xcs6IvUdfbd20i2f++QFzXlhEQ10Ir8/DhGNH8OWvHMtBhw5Ka+d1RbCW+1e+xxPrFhJMNNcc138E/zHmRI7sm5tNeyJCeUHXqHiQyiS4WSJyZtojMTlnbf0HLK+ZQ1RDKA5KjKiGuPethSzduv3zxAAQisaoDYb49iPP2azgLqYkkM+pQ0bidSWv893UBaMO+fz35x6fx7WX3MWsJ+dTW9NILOYQbAzzztzl/PBbD/DHXz1DLJaeJUTW1O7g3Ffv4uE1H1AfDRNTJabKW9tX8fV3HuDRNfPTctyeJJXkcAPxBBEUkd0iUisiqa0da7q0T6tmEdXmax0tWjGYUDT5f/qd9Q0s3rw93aGZTvbb409jQEEhXlfyr4SA28OdU8+hV2Kew9wXF3HPX14hHIoSjez9WVBVQsEIb81Zyl9v6fjS0ftyVLnmnQfZHQl+Ph/j82MDwViU33/6Ckurt3b6sXuSVIr99FJVl6oGVLUo8bhr3BeZDgk7jUm3B0Mttzu7RNhWU5uukEyaFAfymHX+1zh/5CEE3B56eX308voJuD0cUTqAh868iClD4ksyxKIx7vrDi4SCrS9xEgpGeHnWJ2zbUtWpsb69fRW1kWCrU8bCsSj3rux4wZuerM0+B4k3Gl4BDFfV34jIYKBcVeelPTqTVaN6ndDaDp8AACAASURBVMD24HKiuveaLwX5Ieobko+uiDoOw/r1yUR4ppP19gf4/Uln8IvJU1hUuZWw4zC8dzFDi/b+9/zg7RVEo6mNTVFVnn98Pt+88fROi/OlzUupb2PorYPy+rYVnXbMniiVZqW7iC9rcXnicR3w17RFZHLG2OIz6OUtwy1frDXvET8nHFZPIMl68S4Rhvfrw+gy65Duygp9Po4dOJSTBw9vlhgAPlm4jsaG1EqsRCMxFr6/qlPjS2VOBpBSJ7tpWSrJ4WhVvR4IAqhqFdB6ZQrTLXhdAS4deieT+n6FEt9Q+vqHc2zp17n5jJ9xypgDyfN+MXQw3+eltFcBd17e8aWCTW4LB1OrK7BHJNK5X9Kje/fH52p7oOWAPGv97ohUhrJGEktcKHy+JpJVMekhfO58JvW7nEn9Lt9r+62XnMXiTdt4ftFy6oIhjhs5lNPGjsLnaXvEi+naygf1wetzEwmn9qVfVl7cqcf/8tDx3P3ZW63uE3B7uXrk5E49bk+TSnL4C/HltPuLyG+JL4r3s7RGZbqEwwYN4LBBA7IdhsmwqWcezgN3v5bSvnn5Ps679OhOPX7/vF5cceAkHlu7oNlyFAAecVEW6MV5Q47o1OP2NG0mB1V9WEQWAlMBAaar6rK0R2aMyUl9S4uYdNwo5r2zstW7BxEo7BVgwuTOW0Z6j+8fehoel5sHVr2PSHz4qhvB63Yztnc5dxxzCfkea/3uCOkOE5YmTJigCxYsyHYYXdru2kZeeX0p6zfuIi/Py3GTRnLY2IG2PLNJqr4uyI1X38PWzdVJaxu7XEJegZ/b7r+GIcNK0xZHdbiR2ZsWs6FuF728AU4fOJbRRaktfW1ARBaq6oSkz1ly6NlUlXseeovHn16AuIRQKIoA/oCXfiWF3PyLCxg8sCTbYZo0Wbd0E26Pm8Gjy9v92saGEPfe8SovP/8xbrcQiykulxCNOhx1zIF866ZpDDjAhjXnMksOpkV33fc6z77wEcFQ86s/ESgsCHDfHVfRv1+vLERn0u38/tfiz/fx2Lo79/s9go1hFry3it3VDQTyfRw58UD69C3sxChNurSWHHJ3SUCTdhU7anl61oeEWxhqqAoNDSFmPvoO3/+vMzIcXfaFolHeXbOBSCzG5OFD6BXofiUyv337lfgCHVtpNZDn4/gpXaPinUld1pJDYqb1A0AZ8WGyM1T1dhEpAf5JvH7EOuDixNwK08mee+njNqvWxhzlldeX8V/fnEqgg18iXcnCjZu59tFnQOMfzpjj8LtzT+fMQ7peCdPWTL3suGyHYHJUKpPg0iUK/LeqjgWOAa4XkbHAj4A5qjoKmJN4bNLgs5XbU5qg5HIJlTt7znpJ4WiU6x59lrpQmLpwmPpwmGA0yo+e+zfba+uyHZ4xGZG15KCqW1X1w8TvtcAyYCBwHjAzsdtMYHp2Iuz+vN7UJqw5qnjc2byOyKz31m3EaaEv7uVlKzMcjTHZkRP/40VkGPF60h8AZaq6Z63dbcSbnZK95loRWSAiCyorKzMSZ3dz/DEjyUuhqaggz09Z/94ZiCg3xByHZAN4VZWoY4sDmJ4h6x3SIlIIPAXcqKq7m46rV1UVkaSXcKo6A5gB8dFKmYi1u5ly/EHcMWNuq/v4/R4uPn8CLldm5zvEHIc3163jnvkLWFa5A4DR/fryzYkTOHn4cNwt1B3oDEcPG0xMmycBEWHq6BFpO64xuSSrdw4i4iWeGB5W1X8lNm8XkfLE8+VARbbi6+4CAS+//tG5+P3JrxH8Pg8HjRrAReceldG4aoJBLnj4Ub7z/Gze37iJmmCQmmCQ+Zs2c+OsFzj3wYeoakxea6IzFPh8/HH6NAIeD3leD363G7/HzXdPPo4hJZ27TlB3F4s5/PbW2fx77qfZDsW0U9bmOSTqRMwEdqnqjU22/wHYqao3i8iPgBJV/UFr72XzHDpmyfIt3HXva6xYU4HX40ZVEREuOPtIrrz02JT7JjpD1HG44KFHWLFjR7MqX3t4XS4OLCnh2a9egdedvth21jfw8vKVhKMxpoweweA+PadprbM0NIQ5+/I7mDzxQH770/OzHY7ZR05OghOR44G3gMV8scrrT4j3OzwODAHWEx/Kuqu197Lk0Dm2VdSwZVsNfp+H0SPKMpoU9nhl1Sq+N/tFGiKtVxnL93q55YwvceaY0RmKLH0aoxFcIvjdWW/lTYvdtY3kBXxZ+TyZ1uXkJDhVfRuS9vtBfJE/k2ED+vdmQJY7nv8+b0GbiQGgIRLh7nnz9koOqkFofAFtfAKcanCXIfmXgX8qIp3zUY9EYzw9dxFPzvmY2oYQhxw4gK+fdwxjD2zf6rSOKo+v+oS7lr7LlvrdKDC2uD83HH4Cpw4a1Smx5oqiXnnZDsHsh+55qWKSclR5Y+1aHvzoY7bsrqV/QQFXHHkEU0eMwJPGDt72WL4j9ZFnq3Z+cUOpkSXorquACGhDfGNsNRr5BKQISh5CPIM7FFs05vCd3z/F0jXbCCYWm3v7ozXMW7KB//3PszhxfGqd1arKDW8/y5zNq/ZacvrTqu185+1nuf7QyVx/qE1OM9llyaGHqAuHufKJJ1mxY+fnV+Yrd+7ko61bGdS7iEcvuZjivOxf4bWnlXNPk6jGNqO7vgqaZIKa1oM2orsuhX4vIa79XyPqtfkrWbZ2++eJAeKzp0PhKL/5+0u8eOe3UpoP8uLGz5i7ZVXSWgSNsQh3fvoupw8ew6jeuVduVSMrwdkMnjGIu/2L9ZmuIzcuF03afXfWbJZWVDZrsmmIRFi7q4pvPv1sliLb2/A+qa/iOaQ4PnJI62aABlvZ0wGnFm18qkOxPfvGYhpDyZu8YjFl8cotKb3P3UvepyHactNZxIlx//L5+xVjuqizC2fHl9GdF6LV30MrT8ep/h6qbTcBmq7JkkMPsKG6mnfWbyAcS75URsRxWFpRwdKK7I8a/sbECeR7256Yl+/18s2JE+JfTo1PE1+NpTVBqP9Hh2JrKTEAIBAMp/ZFuWr3jlafj6ny0Y7UEk2maNX1EF0KBBN3aCEIvorW/jnboZk0seTQA8xZvabNfcKxGK+sXJWBaFp3xuhRlBYU4G5l0p1bhD55eZw1ZjQ4NdDm8oEJTseS3/FHDMfvTd4SG4nGUu6U9rnaHrWT58mdFl+NboDIpzRPwEFofITusOy/ac6SQw8QjEbbXPbBUaU+xSvfdPK53Tx66cUM7FWU9A4iz+uhrFchj116CQGvF8QPpFbovqNdbBdMOYKA39OsOl7A5+HCKUfQuzC1PpszBo/B3UqFvXyPlwuHH9ahWDuVUwnSQslNDQLZ/9yYzmfJoQcY2beEQAtXvHvke72MLs2NDtCywkJevOpr/HLqFEaUlOAWwS3CsD59+MWUU3j56qs4oCjesSyuXuAe1uZ7rgsWccuW07jy3b9y08IHea9yRbuveHv3yuPeX17GYSPL8Xnc5Ae85Ae8XD5tAt+57KSU3+fascfgcyX/9xDA7/Ywffih7YotrTyjQMPJn3MfgLSUOEyXZpXgeoCo4zDprr9REwy1uE++18u8//wP8lJo78+0PZ/RlupZa+NzaM3PgeRLarywawi3bDyKqHqIJZqg8tw+ju03mt+OuxSXtP8aaWd1PbUNIcr7FeH3tf+O5N1t67j2jadQ9PPO6QKPj3yPl0dOvZyROTZSydl9CzQ8wt5/xwHo/UdceadnKyzTQTk5Q7ozWXJo22tr1vDt52YRjDbvuA14PPzu9NM4d+zBWYis41QVrb4BQm+wb4LYHs7j4mVnENLm7fwBt5ebDj6H4/MPZunSzQT8Hg4/Ygi+/fiy3x/1kTDPrP2Ud7atw+tyc/rg0Zw+eDTeFPokMk1V0YaZUP93cHaBexjS6/tIYEq2QzMdYMnBAPDehg38eu5rbKiuwet2EXUc+hcU8vNTTuaUEQdmO7wOUXXQ+nuh/h4gTLyBxmHG1kN5cPtQIi18zgfu7Evk4Xi9bFXo27eQO/76NYqKsj/nw5h0s+Rg9rJ2VxXb6+rom5/PyL4lLTbXdEWqMYgsAt0Nrr78aPHHvFaxtIWdofDOQqThi/P3eFycfMpYfvyTczIUsTHZk5NrK5nsGV7Sh+ElqU8260pE3OA78vPHgwo24hEX0ST1GQiDBPdOjNGow6qV29IdpjE5z0YrmW5t+qBJuFvocA4EvHg8ez/ncgnl5VazwRhLDqZbG1zQl+tGnUrA7d1rCeA8t5exxYP4/g/OxO/34HIJXq+bggI/1//XaVmL15hcYc1Kptv7yvATObR4CA+ueZOVtdvo4yvg4qGT+VL5EXhcbgYP6sfCBWvx+z2cMmUsJSWF2Q7ZmKyzDmljjOmhWuuQznYN6ftEpEJEPm2yrUREXhGRlYmfXbrnNBQLUxdtSFqw3hhjclW2m5X+AdwJPNBk24+AOU1qSP8I+GEWYuuQhbsW88SmF1ldtwGXCB7xcGrZcUwfeBp9fFaL2BiT27J656CqbwL71oc+D5iZ+H0mMD2jQXWCh9c/y59W3MvKunU4OEQ1RtAJ8dK2N/jux//LtmDq1c6MMSYbcnG0Upmqbk38vg0oS7aTiFwrIgtEZEFlZe582X5UtYTZW18j5DRfqCyqMeqiDfzv0r92+2WOG8IRdtTVE06yXEdP4zjK/Hlr+Odj7zN71sfU1DRkOyRj2pTtZqVWqaqKSNJvUVWdAcyAeId0RgNrxVObXkqaGPZQlKpwDctrV3Nw0cgMRpYZb61cx/+9/gGLNm3D7RIchdPGjuC6k45mdFluLSaXCevWVvLDH/yT+voQ4XAUr9fNnXe8wle+eixXfMXqRJvclYvJYbuIlKvqVhEpBzJWnszRKBvr32NVzYs0xnbhceUxpOAERhZ9CZ+7oM3XxzTGZ7VtF9YJOSHm7fqk2yWH2159hwfe/ZDGSPxuIZrog3/p05XMXb6G2y89mxNHD89ihJlVVxfkxhseorb2ixKmsVj8L+Xhh9+jX2kRX/pSDtVtMKaJXGxWeg64MvH7lUBGihtvrHuPR1efxxtbf8P6+jepCH7Klob5LNhxF4+uOZePdtzXZlNQ1IkBba9TpEAw1vLdRVc0d9lqZjZJDE05qgQjUW58bBbbd9dlIbrsePnfi4lEkhciCgUj/OO+N7t986LpurI9lPVR4D1gjIhsEpFrgJuB00RkJXBq4nFarat9g7lbf0bIqSGqe7cHRzVITEMsrnqE9yr+1Or7+Fxe8j2BNo/nd/kYkn9Ah2LONXe9/j7BJImhqZijPPrBJxmKKPvefXclwWDLVdKqquqpqqrPYETGpC7bo5UuU9VyVfWq6iBVvVdVd6rqVFUdpaqnquq+o5k6VWO0ije2/ZqYtlwIB+JJYtXul9hQ93aL+4gI0wachFdab61TVU4snbhf8eaiXfUNrNi+s839wrEYz3zcwgqp3ZDb3fZ/r1T2MSYbevwnc0XN8ynvG9Ugi3Y91Oo+Z5VPodBTgKuFv1q/y8dFg8+kwJPfrjhz2e5gCG+KX3INoc5pTnPUYVPDJtbUraU2Utsp79nZTjllLHl5LVfWKy8vpnfv7vM5MN1LLnZIZ9Sy6n+1edcA4MKhn7ueXrE32bzz+/QpOJ88/+RmtRB6eQu45fAf8Ltlf2NLsIKwE0FR/C4finLRoGlMH9i9yir2yc8jEkttBnhxfseK6Djq8MLWl3hp278JOxFc4iLqRDik91guHXwJ5XkDOvT+nemUKQfzj/vfJBSK4jh79y34/R6u+w+romZyV49PDo2xtlutSlz1jPbFB025RalreIT6xmfwuMsYXPooXs/gvfbv6+/DH8f9hNV1G1iwaxGNsRAD88s4ru9R5Hu6X4Wx3nkBjhp6AO+v2djqfgGvh0snHb7fx3HU4faVd7J09zLC+wwX/qR6Mct3r+AnB/+QoQVD9vsYncnv93LHX6/kV794irVrKz9vQhIRvnPD6RwzuXuNVjPdS49PDoILpeWr3iJXI6N9Fbj3mm6hqNYTia5j/fZzGF7+Fm5Xr2avHVE4hBGFqX9ROY7Dig/XUVVRgz/Px5jxwynoIs0O354ymY83bm21U9rrdnPB+EP3+xhv7XiHZUkSA8TnjwSdILevvIM/HfH7nKluV1rai7/+7SrWrdvB2jUVFBYGGHfkULze3KsTbUxTPT459PIOpCayvsXnh3t27pMYmnLYuSXEK3fezJoPC1BHGXnkUM659lQGjky9eSMWc3j27jk8ecdLNNYGEZcAQjQS5YTzJnDlT6fTf3Df9p1Yhh01dCC/OGcKv35+LpFYjFiTZhS/x43P4+EfV3+Z4vy2R3Mlo6rM2jK71QmGAHXRepbVLmds0cH7dZx0GTasH8OG9bxJgKbr6vFLdq+omcX7FbcT1cZmz/mIclRgI64kySEWER77xUEsfL4MEKLheJOBx+vG5XYxfuqh/PiB6wnk+1s9fjQS5ReX/IUl768i1Nj8i8/lFvIL8/jjCz9k2NiB+3WOmbS6Yif/ePdD/r1kBcFIjD75AS6deDiXTDqckoL9vwtqjDVy/YffSWl123MPOJsLB52/38cypqdobcnuHp8cok6Qf665kJBT0+y5AglxqH8Lnn2Sg+PAvd8+jOVv9yUSTN484At4GXHEUP7w8k/x+prfoEWdMMHYbmb+9CVefujdpIlhDxEo6tuLBxbdgj/P184z7B7qo/V856PvEtXkk8qaOrv8TC4afGEGojKma8vZeg65wOMKcMag2/C68tl3dnNY3bhonjyXvtGXz94paTExAISDEdYs3sC/Z76x1/aYRnlj+13cvfJ87ll8FbMfeK3VxACgCuHGMG88PT/1E+tm8tx55Lnb7swPuAIMKxiagYiM6d56fHIA6BsYxblD7qE8bzxu8eGW+NV5FD912vwL6dW/DyXc2HZ3TaghzBO3zt5riYRXt/6RT6tnE9UQG//tgRb7M/bWWB/iqTv+neIZdT8ucXF62Wl4peV5A3v2O7J4XIaiMqb76vEd0nv09g1h2uDbqYtsY33dmwRj1XhceZR68qnb/QM00ScRiwprPyxO+X13batmx+YqSgeVUBfZwcraN4hpfEmF2tU+nGDq+Xn7hrZnITe1pmYXO4L1jC7uR7G/6w+hPX3Aqby78z0qQzuIavNRUT7x8s0Dv47HZR9rYzrK/hfto9A7gEP6XLzXtgbfALbsvA5HQ4SDYVwuJeakNlTS7XHTWB9flbMytBq3+D5PDuJpX39PqqMzN9XVcN1r/2J1zS68LjfhWJRLRh3BLydNxe3qujeLAXeAn4/9KfeuvZ9PqhfhFjeKIggF7nyuGn4lRxTbKqfGdAZLDinIDxzHiAM+oT44h8aCJcBHkKQvIploOEpRSSEABZ4StEmHau8xYdx5DrHG1L6wB48ub/t4jsPFLz3Ctoba+GqosfgV9hOrFtHL5+P7409K6Vi5qsCTz3dGXU91uIYlu5cScSKU5w1gdOGonJnbYEx30HUvIzNMxE1h3umU9vkux54zMTEXoW0jxg2luLQIgFL/SAq9pUii43vAKalXBMsr9HPRDWc02x4MvsaOyguorJhGXd09zN20ippQEGefUWiNsSj3L1tIONb2aJ+uoNjXm+P6Tebk/icyptdoSwzGdDJLDvvhou+dhc/fescoQKDAzyU3nfP5YxHh3EG/pcDTF6/k48vzMPKr9bgDrd+FuNwuivsVMfnMvTtag8G57Nr5DcLh94lEPmF3ze9Yuv1fhGLJZyk7quwKWolKY0zbLDnshzETDuSS75+DP7/lOQf+fD+nXDKZyWeP32t7se8Arh7xMNMG/pTjS6/luz+/iVMvOoFAC+/l83spGdCb3z//fTzevVsBa3f/GWg6ea+Rfu5X8btbbi0sDnT9jmljTPpZn8N+uuLH0ykp6819v3icSChKsD6EouQVBhARLrnpbC656ZykzR0ucTO88OjPH994+wSO/tIRPHbrbNYu2YTX58FxFK/fw/TrTuW866ZSmGSNJdXmdwHHl67Ev8JN/T43DwG3h4tGHkaglcRhjDF75OwMaRE5A7gdcAP3qGqLFeE6MkO6o2Ixh/n//oQ1i9bjOMqQMQcw+Zyjks6KTsXOrdVUVe7Gn+fjgOGluD0tT7Srrb2D2trb4POlP3z4/cey0307V776BLvDIUQg4sSYMnAEt514Tqt3FcaYnqXLLZ8hIm5gBXAasAmYD1ymqknLiGUzOWSTqsPuml9SXz8TUHz+4ygpmYHLVYSjysKKzewI1nNoSRmDe6U+N8MY0zO0lhxy9TJyErBKVdcAiMhjwHlAz6kx2UQwVsO8yjtZX/cmbvFxUO/zGNf3KlzioXfxbyjq/T+AgzQpT+oSYWLZoOwFbYzp0nI1OQwEmlaO2QQc3XQHEbkWuBZgyJDcKO6SDqoOL2y8nt3hTThEiVDP4qpHqYts48TynwEg4sLGFhhjOlOX/UZR1RmqOkFVJ5SWlmY7nLTZ2vghdZHtOHzRwxzTEGvr5hKMVWcxMmNMd5aryWEz0LT25qDEth6nLrKNZLOxXeKhIboj8wEZY3qEXE0O84FRIjJcRHzApcBzWY4pK0oDh6BJkoOqUuQdnOQVxhjTcTmZHFQ1Cnwb+DewDHhcVZdkN6rs6OMfzoG9puKRPeU1Bbf4mVT6bTyu1qvMGWPM/srVDmlU9QXghWzHkQuOL/sxQwqPZ/XuV/C4AhzU+zz65x2a7bCMMd1YziYH8wURYWjhiQwtPDHboRhjeoicbFYyxhiTXZYcjDHGNGPJwRhjTDOWHIwxxjRjycEYY0wzlhyMMcY0Y8nBGGNMM5YcjDHGNGPJwRhjTDM9cob0lg07ee6Bd3jzhU8INoYpLMpn2iWTmHbJ0RT3Lcx2eMYYk3U5WSa0vdpTJnT2I+8x43ezcBwlGol9vt3n9+Byu/jZnV/lqONHpytUY4zJGa2VCe1RzUpvvbSIv988m3AouldiAAiHogQbwvzmPx9g1dIeWTrCGGM+12OSg6ry99/NIhSMtLpfKBhh5q3/zlBUxhiTm3pMcliyYB21NY0p7fvJB6up2lGb5oiMMSZ39ZjksGF1Beqk1r/i83nYvM5KcBpjeq6sJAcRuUhEloiIIyIT9nnuxyKySkQ+E5EvddYxXS4BSW1f3bO/Mcb0UNkayvopcAFwd9ONIjKWeL3oQ4ADgFdFZLSqxpq/RfuMOXwwSUoxJxUNRxk6qqyjhzTGmC4rK3cOqrpMVT9L8tR5wGOqGlLVtcAqYFJnHHP4QeUcMLRvm/u5XMIJ0w6noFdeZxzWGGO6pFzrcxgIbGzyeFNiWzMicq2ILBCRBZWVlSm9+X/9+gL8AW+Lz4tAfmGAr95wejtCNsaY7idtyUFEXhWRT5P8Oa8z3l9VZ6jqBFWdUFpamtJrDj5yKL+6+yryC/3k5fv2ei6vwEdJ/yL+9Ni3KBvYpzNCNMaYLittfQ6qeup+vGwzMLjJ40GJbZ1m3OSRPPLuz3nzhUXMeWYhdbsbKSntxZmXHsPEkw/C7c61myljjMm8XFtb6TngERG5lXiH9ChgXmcfxB/wctoFR3HaBUd19lsbY0y3kK2hrOeLyCZgMjBbRP4NoKpLgMeBpcBLwPWdMVLJGGNM+2TlzkFVnwaebuG53wK/zWxExhhjmrIGdmOMMc10iyW7RaQSWL+fL+8H9NS1Mnrqudt59yx23i0bqqpJh3t2i+TQESKyoKX1zLu7nnrudt49i533/rFmJWOMMc1YcjDGGNOMJQeYke0Asqinnrudd89i570fenyfgzHGmObszsEYY0wzlhyMMcY006OTg4ickag4t0pEfpTteNJFRO4TkQoR+bTJthIReUVEViZ+drulaEVksIi8JiJLE5UHb0hs79bnLiIBEZknIp8kzvt/EtuHi8gHic/7P0XE19Z7dUUi4haRj0RkVuJxtz9vEVknIotF5GMRWZDY1qHPeY9NDiLiBv4KTAPGApclKtF1R/8Azthn24+AOao6CpiTeNzdRIH/VtWxwDHA9Yl/4+5+7iFgiqoeAYwDzhCRY4BbgD+r6kigCrgmizGm0w3AsiaPe8p5n6Kq45rMbejQ57zHJgfiFeZWqeoaVQ0DjxGvRNftqOqbwK59Np8HzEz8PhOYntGgMkBVt6rqh4nfa4l/YQykm5+7xtUlHnoTfxSYAjyZ2N7tzhtARAYBZwH3JB4LPeC8W9Chz3lPTg4pV53rpspUdWvi921Aty6aLSLDgCOBD+gB555oWvkYqABeAVYD1aoaTezSXT/vtwE/AJzE4770jPNW4GURWSgi1ya2dehznmv1HEwWqKqKSLcd0ywihcBTwI2qujt+MRnXXc89sdT9OBEpJr4C8kFZDintRORsoEJVF4rIydmOJ8OOV9XNItIfeEVEljd9cn8+5z35ziHtVedy3HYRKQdI/KzIcjxpISJe4onhYVX9V2Jzjzh3AFWtBl4jXjulWET2XBB2x8/7ccC5IrKOeDPxFOB2uv95o6qbEz8riF8MTKKDn/OenBzmA6MSIxl8wKXEK9H1FM8BVyZ+vxJ4NouxpEWivfleYJmq3trkqW597iJSmrhjQETygNOI97e8Bnw5sVu3O29V/bGqDlLVYcT/P89V1Svo5uctIgUi0mvP78DpwKd08HPeo2dIi8iZxNso3cB9iUJD3Y6IPAqcTHwJ3+3AL4FniFfdG0J8ufOLVXXfTusuTUSOB94CFvNFG/RPiPc7dNtzF5HDiXdAuolfAD6uqr8WkQOJX1GXAB8BX1HVUPYiTZ9Es9JNqnp2dz/vxPntKZ7mAR5R1d+KSF868Dnv0cnBGGNMcj25WckYY0wLLDkYY4xpxpKDMcaYZiw5GGOMacaSgzHGmGYsORjTiURkejdewNH0IJYcjOlc04mv8ttMk1m6xuQ8Sw7GtEJEvpKojfCxiNydWOodEakTkd8maia8QqcCQQAAAcZJREFULyJlInIscC7wh8T+I0TkdRG5LbHG/k9FZG1iSQ9EpKjp4ybHPCdRf+AjEXlVRLrdwoAm91lyMKYFInIwcAlwnKqOA2LAFYmnC4D3EzUT3gS+qarvEl+y4PuJdfVXJ/b1qeoEVf0f4HXiS0pDfImHf6lqZJ9Dvw0co6pHEp/Z+4P0nKExLbPbXGNaNhU4CpifWMk1jy8WLwsDsxK/LyS+flFL/tnk93uIf9k/A1z9/9u7Y5QGoiAAw/8g2IlYCoKCp9HGJq1YeQQLwSInsMkBREGN2NlbWtqI2HoBO7ESGYtsSPH2ZTFq0vxftfDmwTTLsDswAxy2xG8AN82wtGXgdcb8pZlZHKS6AM4z87jl7DMns2e+mP4ufYwfMvMhIraa2T9LmfncEj8ATjPzronrz5K89Bv+VpLq7oFeMyN/vJN3s+POO7DSEXMBXAFnlfNVJmOlDyox0r+yOEgVmfkCnDDasPXEaKPaese1IXDUNJO3KzGXwBpwXTnvA7cR8Qi8/Thx6Q84lVWas4joAXuZub/oXKQaew7SHEXEANgBdhedizSNXw6SpII9B0lSweIgSSpYHCRJBYuDJKlgcZAkFb4B4BAUQ/6gRwEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'a': np.arange(50),\n",
    "        'c': np.random.randint(0, 50, 50),\n",
    "        'd': np.random.randn(50)}\n",
    "data['b'] = data['a'] + 10 * np.random.randn(50)\n",
    "data['d'] = np.abs(data['d']) * 100\n",
    "\n",
    "plt.scatter('a', 'b', c='c', s='d', data=data)\n",
    "plt.xlabel('entry a')\n",
    "plt.ylabel('entry b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、用分类变量绘图\n",
    "\n",
    "也可以使用分类变量创建图。 Matplotlib允许您将类别变量直接传递给许多绘图函数。 例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['group_a', 'group_b', 'group_c']\n",
    "values = [1, 10, 100]\n",
    "\n",
    "plt.figure(figsize=(9, 3))\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.bar(names, values)\n",
    "plt.subplot(132)\n",
    "plt.scatter(names, values)\n",
    "plt.subplot(133)\n",
    "plt.plot(names, values)\n",
    "plt.suptitle('Categorical Plotting')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、控制线属性\n",
    "\n",
    "线型具有若干属性，详情请参考原文档。\n",
    "\n",
    "## 五、多图与轴协同工作\n",
    "\n",
    "MATLAB和pyplot具有当前图形和当前轴的概念。 所有绘图命令均适用于当前轴。 \n",
    "函数gca（）返回当前轴（matplotlib.axes.Axes实例），而gcf（）返回当前图形（matplotlib.figure.Figure实例）。 \n",
    "通常，不必为此担心，因为所有这些操作都是在后台进行的。 下面是创建两个子图的脚本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(211)\n",
    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fef629bccf8>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(1)                # the first figure\n",
    "plt.subplot(211)             # the first subplot in the first figure\n",
    "plt.title('Easy as 1, 2, 3') # subplot 211 title\n",
    "plt.plot([1, 2, 3])\n",
    "plt.subplot(212)             # the second subplot in the first figure\n",
    "plt.plot([4, 5, 6])\n",
    "\n",
    "\n",
    "plt.figure(2)                # a second figure\n",
    "plt.plot([4, 5, 6])          # creates a subplot(111) by default\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 六、使用文本\n",
    "\n",
    "text（）命令可用于在任意位置添加文本，xlabel（），ylabel（）和title（）用于在指定位置添加文本\n",
    "（有关更多详细示例，请参见Matplotlib图中的Text）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n",
    "\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of IQ')\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$\\\\sigma_i=15$')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = plt.xlabel('my data', fontsize=14, color='red')\n",
    "plt.title(r'$\\sigma_i=15$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 注解文本\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "line, = plt.plot(t, s, lw=2)\n",
    "\n",
    "plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "             )\n",
    "\n",
    "plt.ylim(-2, 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 七、对数轴和其他非线性轴\n",
    "\n",
    "matplotlib.pyplot不仅支持线性轴刻度，还支持对数和logit刻度。 \n",
    "如果数据跨多个数量级，则通常使用此方法。 更改轴的比例很容易：\n",
    "\n",
    "     plt.xscale（'log'）\n",
    "\n",
    "下面显示了四个图的示例，这些图的y轴数据相同且比例不同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9bxLnTBn6icRkBp5gJqgyoPsgaHmBZYeyEPhm9wWqWhb4uU1EinDuT30qQZnwV9XYxt+W7mR9WT3LdtTS0NrFjacXcP38AgbFWZXeQFFR38pX/vohmyoaKchO4oGvFHL6xBy3wzIeEswEtQwYJyKjcRLTQuDygzcSkYlAOvB+t2XpQLOqtolIJnAScGcQYzMeUbS5khsf/Yim9k7G5yRz9pShjKSKb509we3QTD/p6PJz++L1PLFsN11+5b/PmcD188daYwfzKUFLUKraKSI3AK/gNDN/QFXXi8iPgeWqujiw6ULgcVXVbm+fBPxJRPxAFM49qJ4aV5gw1NDawf++spmH3t9JdnI8T103j4lDnT5Ndr9n4Hi/pIYfPL+O4kofi+aM5D/m5TMp1/q2mUML6j0oVX0RePGgZbcd9PqHh3jfe8C0YMZivGH/PYZfvLSRfc0dfH7mcH568VQS46yP+ECzu7aZL93/AbHRUdy18DguOm642yEZj7P/EiZk1pXV892nVrOpopHC/HRuv3AK0/JS3Q7LdNNf/aDWltbztYeW0elXHv5aIScWZIb0eCYyWIIyQbenroXbnl/H6xsrSU6I4VeXTOcLs/KIsoE6PSfU/aBa2rt48L0d/PLlTcTHRPGP609k1kh3W2ma8GEJygTVGxv38j/PrKHa187188dy9SljSB8c53ZYxgUdXX4u+N3blFQ1MTs/nVvPn2TJyRwVS1AmaO55s5hfvbKZnJR4Xvr2KXbze4D7zWtbKKlq4qefm8oVc0daKz1z1CxBmT5bsbOWX72ymaXbarlgei53XjLdGkEMcP/15GqeWVnKWZNzLDmZY2b/RUyfVNS3cvXDK/Cr05/l66eMJj7GOtsOZH95exvPrCzl9InZ3HP5LEtO5phZgjLH7A9FxfzujWIAltx4MgXZSS5HZNx2/zvb+ek/NzJ39BDuuXwWcTE2WaA5dpagzFGrb+ngB8+tY/HqPcwZPYQffXaKJSdDc3sn97xZzPicJO7/yvE2bJXpM0tQ5qi8s7Wa6x5ZQWNrJ189aRTfP38y0dZ8fMDz+5Vbn11HbVM7f/6PQpLi7V+L6Tv7FJleUVUeem8HP1zijED1t6vmcMq4LJejMj0JzApwK5CqqpeE+nivb9zLsx+V8cXZea6PRm8ih1UQmyPaXNHI1Q8v54dLNjBrZBr//u/5lpxCSEQeEJFKEVl30PIFIrJZRIpF5ObD7UNVt6nqVaGN9GP3/ruE2GjhJ5+b2l+HNAOAXUGZHm3Z28g9bxazZPUe4mOi+frJo7n53InERNv3mhB7EPg98PD+BSISDdwDnIUzGegyEVmMMzDzLw56/9dUtbJ/QoUNexpYuauOz84YZjMgm6CyBGU+ZcveRv7rydWsLatncFw0XzlxNDeeXmAjQvQTVX1LREYdtHgOUKyq2wBE5HHgIlX9BXDBsRwnGLNT+1X56dJWEmPgnMy6fhmZ3kszHnspFoi8eCxBmQM2ljfwy5c38daWKuJiorj1vElcNHMY2ckJbodmYDiwu9vrUmBuTxuLSAbwM2CmiNwSSGSfEIzZqR9+fwfb6tdz63mTOP8zY3r1i/SVl2Y89lIsEHnxWIIy7Gtq53f/KuZvS3fQ0aV87aTRXHXKaIanDXI7NHOMVLUGuPZI2/VlNPOG1g7uLSqhIDuJq/spOZmBJeg3E450I1dEviIiVSKyKvD4erd1XxaRrYHHl4Mdm/m0d7ZWc87/vcUD727n/Gm5vHfz6dx24WRLTt5TBozo9jovsKxPVHWJql6Tmnr006Dc82YxFQ2t/OqS6X0Nw5hDCuoVVE83cg8xO+4TqnrDQe8dAtwOFAIKrAi8d18wYzSOuuZ2vvnoSt4trmFcdhK/v3wWc0YPcTss07NlwDgRGY2TmBYCl/d1p325gtpY3si04anMtBHKTYgE+wrqwI1cVW0HHgcu6uV7zwFeU9XaQFJ6DVgQ5PgGvDWldTy9pZ35vy5i6bZavn7yaJ66dp4lJw8RkceA94EJIlIqIlepaidwA/AKsBF4UlXX9/VYfbmCamnvtEGBTUgF+9PV2xu5XxCRzwBbgP9U1d09vNfmhA6iu9/Yym9e2wLAaROyuOH0cdap0oNUdVEPy18EXuzncHrU1NZFbmqs22GYCObG158lwGOq2iYi3wAeAk7v7ZuD0TTWDW7F09alrKnq4tUdHWyt8zMjK5pFYzoZmt5M4/bVFG3v95A+xf5W7ulLFV9LR5eNt2dCKtgJ6og3cgOti/b7C3Bnt/fOP+i9RQcfIBhNY93gRjzr99Tz9YeWU17fxvC0QfzwwtEsmjuS9995e8Cfm8PxWjyhdKxTvje1dbKrtpnzpg0NUWTGBD9BHfFGrojkqmp54OVncerTwalb/7mI7K9zOhu4JcjxDQhLt9XwxLLdPPuR893gvi/N5rSJ2cTaCBAmSLbsbaTLr0zPS3M7FBPBgpqgVLVTRPbfyI0GHlDV9SLyY2C5qi4GviUinwU6gVrgK4H31orIT3CSHMCPVbU2mPFFMr9feXXDXn7z2ma27PWRHB/DuVOH8l9nj6cgO9nt8IxHHWsV36rddQBMHGqfLRM6Qb8Hdagbuap6W7fnt9DDlZGqPgA8EOyYIllzeyd/X7qT+97aTrWvjaEpCfzggslcMXekjYtmjuhYqvjqWzr49SubmTg0mZFDEkMYnRnorI1omOryK/9cW86Pl6yn2tfOyQWZXHb8ZM6dOtQGczUh4/cr1/19BU3tXdx24WSbzt2ElCWoMLOvqZ173yrhmRVlVPvamJybwq8umcH8CVn2z8IctaOt4nt5fQXvldTwPwsmcuLYzNAGZwY8S1BhZGdNExfd8y51zR2cXJDJ5XOncNbkHGv8YI7Z0VTxqSp3v7GVnJR4rj5ldD9EZwY6S1Bh4pkVpdzy7FoGxUZz75WzWDA11+2QzACzsbyRTRWN3GJzgpl+YgnK40r3NfOrVzbz/Ko9TByazJ++NJv8jMFuh2UGoKdXlBIdJVwyO8/tUMwAYQnKoxpbO/jly5t4cnkpUQJfOiGfG08vIDvF5mYywXM096De3lrFnFFDyEiKD31gxmAJynNUld++vpX7395GS0cXlxaO4FtnjGOYTX9hQqC396DaOrsoqfIxd4wNKmz6jyUoD/G1dfKLFzfyyAe7OHFsBjefO9F66htPeLe4Gr9iLfdMv7IE5REfbKvhO0+soqKhlatOHs2t500iKsqajRtv+GhXHVECZ0zKdjsUM4BYgnLZ21ur+PmLm9hY3kBaYizPXHcis2wCOOMx+5rbSR0US3yMjU5i+o8lKBc99N4Obl+8nqzkeG67YDIXzMglO9kaQRjvqW/pJC0xzu0wzABjCcoF9c0d3L54Hc+t2sOUYSk8c92JNm6ecUVvW/HVNbeTMsgmJzT9y3rb9bN3tlbz2Xve4YU15XzrjHH843pLTsY9vZ3yvaGlgzRLUKaf2RVUP2nqUL756Er+uaaczKQ4nvjGCczOtya7JjzUtXQwKtM6iJv+ZQkqhFSVos1VLFmzh9fXNdPY0cw3Th3Dt04fx+B4O/UmfNQ1d5BqV1Cmn9l/yRBQVZ79qIw/v72djeUNxMVEMT0jmv++6HjmjslwOzxjjopflYZWq+Iz/S+oCUpEFgB34cym+xdVveOg9TcBX8eZTbcK+Jqq7gys6wLWBjbdpaqfDWZs/aWysZUr//IBW/b6GDkkkZ9cNIUvFo5g6btvW3IyYam8SVGFETY5oelnQUtQIhIN3AOcBZQCy0Rksapu6LbZR0ChqjaLyHXAncBlgXUtqnpcsOLpb51dfp5btYfvPrUagJvOGs/188faqM8m7O1rVQAbpNj0u2BeQc0BilV1G4CIPA5cBBxIUKr6ZrftlwJXBvH4/a6908+zH5XywfZa3t5aTVVjG8PTBvG/l87gBLtaMmGgN83MmzucBGX3oEx/C2aCGg7s7va6FJh7mO2vAl7q9jpBRJbjVP/doarPHepNInINcA1ATk4ORUVFh9y5z+frcV0w1LT4uX9dGxtq/CTFwpSMaC4dG89x2ULrrrUU7erfeI6Gl2IBi8dNvRkstrnTSVDJCXbL2vQvVz5xInIlUAic2m1xvqqWicgY4F8islZVSw5+r6reB9wHUFhYqPPnzz/kMYqKiuhpXV+9tLacHz2zBl+bn2+fMY5vnzHuiOPmhTKeo+WlWMDi8bq2Lufn4DhLUKZ/BfMTVwaM6PY6L7DsE0TkTOBW4FRVbdu/XFXLAj+3iUgRMBP4VIJyU2tHF39fupOf/nMjGYPjeOHGU5g8LMXtsIwJqfYu5woqIc7up5r+FcwEtQwYJyKjcRLTQuDy7huIyEzgT8ACVa3stjwdaFbVNhHJBE7CaUDhGVWNbSz681KKK31Mzk3hoa/NISvZJm4zka+tC6KjhDhr8GP6WdASlKp2isgNwCs4zcwfUNX1IvJjYLmqLgZ+BSQBT4kIfNycfBLwJxHx4wy/dMdBrf9c9fyqMr7/7DpaOrq498rZnDMlh0D8xkS89i5lUGy0feZNvwtqpbKqvgi8eNCy27o9P7OH970HTAtmLMGyrcrHd55YxeC4GBbfcLJV6ZkBp60LBsXZeJGm/9k1+2H8a9NePnfPu8RGRfHo1XMtOZkBaf8VlDH9zZrlHMLOmibuebOYJ5eXkhQfwwvfOpnxOcluh2WMK9q6INGuoIwLLEEdpKqxjQt+9w7N7V1cMjuPG04rsFGcTdgRkc8B5wMpwP2q+uqx7qu9CxLiLUGZ/mcJqpviSh/ffvwjmto6efIb8ygcZdNhmP4nIg8AFwCVqjq12/LDjnXZXaCj+3OBFrK/Bo49QfmVFLuCMi6wBBXwbnE11z+yEoC7F8205GTc9CDwe+Dh/Qt6GusSJ1n94qD3f61bN47vB953zNq6sHtQxhWWoIBHPtjJrc+uIyclnj99qZDjRqS5HZIZwFT1LREZddDiQ451qaq/wLna+gRx2oTfAbykqiv7Ek9bl1orPuOKAZ+glqzew+3Pr+eEMUO498rZpCXGuR2SMYdytGNd3gicCaSKSIGq3nvwBr0d17Klw099TZVnxif00liJXooFIi+eAZ2g/r2lihsf+4hRGYn86cpCUhNttGYTGVT1buDuI2xzxHEt65s7qH/5VSaPHcn8+ZNCEepR89JYiV6KBSIvngHbD2rFzn3815OrSY6P4cVvn2LJyXhdr8a6PFoicqGI3FdfX3/I9VU+Z7jMSbnWB9D0vwGZoF5Ys4dF9y0lSuDxb5xAoo3SbLzvwFiXIhKHM9bl4r7uVFWXqOo1qamph1zva+sEIGWQlRHT/wZcgnr0g13c8OhHjMkazJIbT2bKsEMXTGPcIiKPAe8DE0SkVESuUtVOYP9YlxuBJ1V1fRCOddgrqPqWDgCS4q2GwfS/Afe16LUNFeSmJvDcN08iwZrOGg9S1UU9LP/UWJdBONZhJyzcWdMEQH5GYjAPa0yvDKgrqNqmdlbs3MdxI9IsORnDka+gGludKr50a91qXDAgEtS2Kh+3/GMtZ/7m3zS1d3HtqWPdDskYTzjSPai2Tj8AsdE21YbpfxFfxXffWyXc+fJmoqKEc6YM5asnjWKGdcQ1plfaO/3ECDYXlHFF0K+gRGSBiGwWkWIRufkQ6+NF5InA+g+695gXkVsCyzeLyDl9jeXJ5bv5+YubmJWfzjvfO43fLZrJrJHpfd2tMRHjSFV87Z1+YgZEPYvxoqB+9LqNF3YuMBlYJCKTD9rsKmCfqhYAvwV+GXjvZJyms1OABcAfAvs7Jvta/fzkhQ3Mzk/nb1fNITsl4Vh3ZUzEOlIVX0eXn1hLUMYlwf7oHRgvTFXbgceBiw7a5iLgocDzp4EzAuOGXQQ8rqptqrodKA7s76jta2rnz2vbaOvw879fnEF8jDWIMOZYtHf6iY6y6j3jjmAnqEONFza8p20CfTvqgYxevrdXfrRkPRtr/Pz3ORNsLidj+qDdrqCMi8KukURvBrg8c4gyYqoyzr+LoqJd/RzhoXlpEEcvxQIWj5tE5ELgwoKCgkOuv37+WCbH1fRvUMYEBDtB9Wa8sP3blIpIDJAK1PTyvb0a4BIgKcIGTQwmL8UCFo+bjjEEIKcAACAASURBVNRRd1xOMmXpVkVu3BHsi/fejBe2GPhy4PklwL9UVQPLFwZa+Y0GxgEfBjk+Y4wxYSKoV1Cq2iki+8cLiwYeUNX1IvJjYLmqLgbuB/4mIsVALU4SI7Ddk8AGoBP4pqp2BTM+Y4wx4UOci5fwJCJVwM4eVmcC1f0YzpF4KR4vxQLhE0++qmb1dzChtP8eFHAZsLWHzcLl7+MGL8UC4RNPr8pSWCeowxGR5apa6HYc+3kpHi/FAhaP13ntfHgpHi/FApEXjzUgNcYY40mWoIwxxnhSJCeo+9wO4CBeisdLsYDF43VeOx9eisdLsUCExROx96CMMcaEt0i+gjLGGBPGLEEZY4zxpIhMUEeakyoExxshIm+KyAYRWS8i3w4sHyIir4nI1sDP9MByEZG7A/GtEZFZIYgpWkQ+EpEXAq9HB+bfKg7MxxUXWN7j/FxBjCVNRJ4WkU0islFE5rl8bv4z8HdaJyKPiUiCm+fHy6wsWVk6QjyhLUuqGlEPnBEsSoAxQBywGpgc4mPmArMCz5OBLTjzYd0J3BxYfjPwy8Dz84CXAAFOAD4IQUw3AY8CLwRePwksDDy/F7gu8Px64N7A84XAEyGI5SHg64HncUCaW+cGZ4T87cCgbuflK26eH68+rCwdiMnK0qFjCXlZcr0QhOAPOA94pdvrW4Bb+jmG54GzgM1AbmBZLrA58PxPwKJu2x/YLkjHzwPeAE4HXgh8QKuBmIPPEc6wVPMCz2MC20kQY0kNfIjloOVunZv907oMCfy+LwDnuHV+vPywsmRl6QjxhLwsRWIVX9DmlToWgcvWmcAHQI6qlgdWVQA5geehjvH/gO8B/sDrDKBOnfm3Dj5eT/NzBctooAr4a6Ca5C8iMhiXzo2qlgG/BnYB5Ti/7wrcOz9eZmXJylKP+qMsRWKCco2IJAHPAN9R1Ybu69T52hDyNv0icgFQqaorjuJtvxGRn4YopBhgFvBHVZ0JNOFUQxzQX+cGIFA/fxFOYR8GDAYW9MexTe+5VZZEZIeInBl4fixlCRG5V0R+EILwBlxZisQE1at5pYJNRGJxCtQjqvqPwOK9IpIbWJ8LVPZDjCcBnxWRHcDjOFUTdwFp4sy/dfDxynA+WMgn5+cKllKgVFU/CLx+GqeQuXFuAM4Etqtqlap2AP/AOWeHOz8jAnGG4vx4mZWloy9LI1T1WuAXOJ+VqSJSGqR4BlxZisQE1Zs5qYJKRARnGpGNqvqbbqu6z331ZZz69P3L/yPQyuYEoL7bJXqfqOotqpqnqqNwfvd/qeoVwJs4828dKpb906l2n58rKFS1AtgtIhMCi87AmVKl389NwC7gBBFJDPzd9sdzuPNzqPnLBgIrS0dflj7xWQlGHN3iGXhlKVg3zLz0wGm9sgWnBdKt/XC8k3Euq9cAqwKP83DqV9/AmcbgdWBIYHsB7gnEtxYoPML+/wfn20cjzo3OK4BmIKPbNrNw6qdjcVrSvAs8BXQA2wIfiG2B163AVYH3JQA7cObm+hCnxdbVwP75uhYDw7od5+xADPXAH4B/E2hVdJj4jwOWB87Pc0B6sM7NMf69fgRsAtYBfwPiA7/3h4Hf+ykgvtv5eSqw/ENgjNufbytLofu8BMrCmYHPxP8BewKPp4EXA9uMwfnn3Am0ANcGYp4c+Kw0BMrrlMB6P+ALPIb1Mb4BVZZcLwD2OOIHYALOjcVhgdejgLHAiwSabwaW/xb4XeD5VwKF56s4TYV/GihQ9wQ+QGfjJLukwPYPAj8NPD8dp3XNrMC2vwPeCqzLDBS+z+PUh38bJ+EdNkHZwx7h8uiWoH4MLAWygSzgPeAngW0W4DRGmAIkAn8PJKiCwPru5Wk+TrWc679bOD4isYov0nThJIrJIhKrqjtUtQSnP8SV4HQkBBbhfIPZb7uq/lWdWYmfwKn7/bGqtqnqq0A7H1ftdXcFzkzIK1W1Dadp8bxAi6rzgPWq+g91WuHcjVNQjYk0V+CUl0pVrcK5UvhSYN2lwF9Vdb2qNgM/dCnGiGcJyuNUtRj4Dk4hqBSRx0VkGE697mQRGY3TT6ReVT/s9ta93Z63BPZ18LKkQxxyGN1mKVZVH86NzOGBdbu7rVOcG7fGRJpPlIPA82Hd1nVvvt39uQkiS1BhQFUfVdWTgXycqoRfqmorTo/tK3G+2f3tMLs4GnsCxwEg0M8iA6dOvRynVc7+ddL9tTER5BPlABgZWAYHlQM+2VLuYAOlQU1IWILyOBGZICKni0g8TuOG/TddAR7Gud/0WYKXoB4DvioixwWO+XOcIVJ2AP8EponI5wLNRL8JDA3ScY3xkseA74tIlohkArfh3GsC54vhV0VkkogkAofr87QXyBCR1NCGG5ksQXlfPHAHTsOFCpybtrcAqOq7OMlqparu7HEPR0FVX8cpcM/gfFMci9PEFlWtBr6IM/ZXDU6rpeVAWzCObYyH/JSPW8utBVYGlqGqL+Hcf30Tp0Xa0sB7PlUOVHUTTrLbJiJ1gep500s2YWGYE5F/AY+q6l9cOHYUzj2oK1T1zf4+vjFeICKTcJpZx+vHQ/yYILArqDAmIsfjNAd/oh+PeY44Q/7HA/8Pp6/F0iO8zZiIIiIXB6aPSAd+CSyx5BR8lqDClIg8hNMp7zuq2tiPh56H0/GvGrgQ+JyqtvTj8Y3xgm/gDClUgtMV5Dp3w4lMVsVnjDHGk+wKyhhjjCfFHHkT78rMzNRRo0Ydcl1TUxODBw/u34AOw0vxeCkWCJ94VqxYUa2qWS6EFHJWlo6Nl2KB8Imn12XJ7bGW+vKYPXu29uTNN9/scZ0bvBSPl2JRDZ94gOXqgc99KB5Wlo6Nl2JRDZ94eluWrIrPGGOMJ1mCMsYY40lhfQ/KmL6qb+lge3UT26p87KhuoqG1k/kpbkdlTHhbvHoP6/fUM29Q3/ZjCcpEvNaOLrZXN1Fe38LKnXXsqWuhvL6VsroWdtU2H9guSmBoSgKnzLWKBWP6Yum2Gl5dv5d5J/ctxViCMhHD71fK6loorvLx4fZaSip9bNnbyK7aZvyB7n4xUUJOSgK5qQlMz0vlsuNHMD4nmVEZiYzKHExsdBRFRUWu/h7GhLuOTj+x0dLn/ViCMmFn/xVRSZWP4kofJVVNlFT62Fbto7XDGeg9NloYMSSRycNSuOi44YzNTmJYagITc1NIirePvTGh1NHlJza67zURVlKNZ/n9yqrSOrZUNH4iGe3e18z+AVBEIC99EAVZSZw4NoOC7CTGZicxyRKRMa7p8KtdQZnI0t7pZ01pHa9t3Mu6sno2lTdS09QOQHxMFGOykpiel8rnZw1nbFYSBdlJjM4cTEJstMuRG2O6c6r4ovh46rpjYwnK9DtVpcrXRum+FnbVNPPSpjZ+v/E91u2pp7XDT3SUMHV4KqeOz+Iz47OYnZ/OsLRBREf1/RuZMSb0Orr8xMVYgjJhQFXZUdPM8h21FG2u4t9bqvC1fTwzQbTArHxYNGckc0dnMGf0EIYMjnMx4oFDRC4ELiwoKHA7FBNBOrqUmCB8obQEZUKisqGVZTv28camvbxXXENFQysAGYPjuHDGMCblJpOXPogR6YnsWL+cs04/0eWIByZVXQIsKSwsvNrtWEzkaOvsirxGEiIyBrgVSFXVS9yOxxydprZOnltVxnMflbFsxz4AUhJi+Mz4LE4Yk8EJY4YwJjOJqIO+WZVttKo7YyJFfUsHq3bXcfmckUBbn/YVtAQlIg8AFwCVqjq12/IFwF1ANPAXVb2jp32o6jbgKhF5OlhxmdBbW1rPUyt288r6CvY2tJGXPoj/PmcCJxdkMnlYSlC+SRljwkNdczsdXcr0vDRorO7TvoJ5BfUg8Hvg4f0LRCQauAc4CygFlonIYpxk9YuD3v81Va0MYjwmxKp9bTz83g7++O8SoqOE2fnp3LVwJieMyXA7NGOMSzq6nD4gsTEequJT1bdEZNRBi+cAxYErI0TkceAiVf0FztWWCUOtHV38felO7np9K41tnZw1OYdfXTKdtERr2GDMQNfRFegsHwaNJIYDu7u9LgXm9rSxiGQAPwNmisgtgUR28DbXANcA5OTk9Dgsjc/n89SQNV6Kpy+xrNzbyV/Xt9HYDhOHRHHZrARGp/pY9eF7rsQTCl6Lx5hw0hm4goqJtEYSqloDXHuEbe4D7gMoLCzU+fPnH3K7oqIielrnBi/Fcyyx+P3Kb17bwu8/KiY+Joo/XnEc507LdS2eUPJaPMaEkw6/cwUVEwYjSZQBI7q9zgssM2Fk9e46fvrPDSzbsY8zJ2Vz5yUzrJ+SMeaQOjqdBBUXHUVHH/cV6gS1DBgnIqNxEtNC4PIQH9MESWtHF799bQt/emsbKQkx/OeZ47nx9IJPNRM3xpj9OgNTB8REiXcSlIg8BswHMkWkFLhdVe8XkRuAV3Ba7j2gquuDdUwTOkWbK7nz5c1sKG/gktl5fP/8SdYIwhhzRPsbSXjqHpSqLuph+YvAi8E6jgm9l9eVc+3fVzIsNYHfXz6TC6YPczskY0yYaAtU8cXHRNHYx315qpGEcZeq8vMXN/LgezsYnjaI1276DIlx9hExxvReU2CczcHxMfStmy5YF39zwBPLdvPnt7czKmMwz1x3oiUnY8xR+zhB9X0aHPsPZADYXdvMzf9YS1J8DM9+8ySb7C/MicjngPOBFOB+VX3V5ZDMANEYSFDB+B9iV1CG9k4/Nz72EdFRwuIbLDkdCxF5QEQqRWRdqPYjIgtEZLOIFIvIzYfbj6o+p6pX4/QrvKwvMRlzNEr3tZCWGBuUGhhLUIY3Nu5l1e46vnPGOMZkJbkdTrh6EFgQqv10G9fyXGAysEhEJovINBF54aBHdre3fj/wPmP6xT/XlDM6c3BQ9mVflQc4VeUPRSUkx8dw9WfGuB1O2OphLMpg7ueoxrUUEQHuAF5S1ZWHOpYNG9Z3XooF3I+nw6/Ut3TQ0dxIUVFRn+OxBDXAFVf6WFtWz63nTSIhtu83NU3IHNW4lsCNwJlAqogUqOq9B29gw4b1nZdiAffjqfa1wauvc+lJE5k/b1Sf47EENcA9udz5n3futKEuRxLZROR14FAn+VZVfT7Yx1PVu4G7g71fYw6nsTV4DSTAYwnKWh71v3eKazh+VDp56YluhxLRVPXMPu7CxrU0nrerthmAzKT4oOwvaI0kemp9ZC2PvEtV2V7tY9rwNLdDMUd2YFxLEYnDGddyscsxGfMJS1bvISE2iuNHDQnK/oLZiu9BDmp9ZC2PvK10XwutHX5GZdrVU18FxqJ8H5ggIqUiclUw96OqncD+cS03Ak/auJbGS7r8yj/XlHPq+CwGxQXnfranZtTtTcsjEzzPrCwFYO5om6K9r3oaizKY+7FxLY2Xba/20dLRxdmTg3c/21Mz6tKLlkfWNLbv9sfy9tpWshOF8k0rKN/kfjxe4bV4jAkH6/c0ADB5WErQ9umpRhK9aXlkTWP7rqioiBNOOoWrX3uFz8/MY/786a7H45VzA96LxxivU1UeWbqL5IQYCrKD19k/1CNJWMsjj2po7aCjS5mal+p2KMaYMPfMyjI+3FHLd84cT2wQ5oHaL9QJyloeeVRbhzNnS0KMjXZljOmbl9aWkxQfw5UnjAzqfoPZzPxTrY+s5ZF3tXZ0ARBvo0cYY/qgrK6FNzZVcvHM4cTHBPf/ic2oO0Dtn/XSrqCMMceqdF8zF//hPeJiovjyiaOCvn/77zRA7b+CsvH3jDHHoqyuhUV/Xsq+pnYe+PLxQW0csZ+nWvGZ/tO6/x6UJShjzFFaV1bPZX96n6b2Lh69ei4njs0MyXEsQQ1QH19B2UW0Mab3uvzKbc+vIypKePraeRQGaVijQ7EENUAduAdlV1DGmF7o7PLz5PJS7nurhB01zdx87sSQJiewBDVgHbiCCnKrG2NM5FFVrv37Cl7fWMn0vFT+cMUsFkwJ/RQ9lqAGqNbO/c3MrYpvIBORC4ELCwoK3A7FeNTehlZueHQly3bs49ypQ/nDFbNwhk0NPfvvNED5AhOLDQ7SxGImPKnqElW9JjXVRhQxn/byunJOufNN1pTW8//Om8hdC2f2W3ICu4IasOpbOoiJEgYHaVh8Y0zkKKny8Yc3S3hmZSnjc5K4a+FMJuUGbxDY3rIENUDVtXSQlhjbr9+GjDHe1dHlZ+m2Gl5aV8HTK0rx+5WLZw7nprPGM2KIO3PGWYIaoOqbO0gdFOt2GMYYl1U2tvLb17bw0roK6po7SIyL5oJpudx83kSykxNcjc0zCUpEFgB3AdHAX1T1DpdDimhVvjZLUMYMQL62TpbvqOX9bTUsLalhTVk9qnDhjGFcMD2XU8dneab7iScSVLep4c/CmdRwmYgsVtUN7kYWudaX1XNOPzQTNca4b29DK397fyfvllSzprSeLr8SGy0cNyKNG08r4OwpQ5k63HsNZTyRoOhhanjAElQItHcpTe1djA3B2FnGGG+oqG/lwfd28NaWKjZWNCDAcSPSuPbUMZwwJoPZ+ekkxnklBRyaV6Lr9dTwNuV731XVNwHC3t3bKSoqdTscT50b8F48xvRGZ5efzbVdfPTaFt4prmblrn2owtzRQ7jpzPGcOy03JAO6hpJXElSv2ZTvfffQ4jeAVubNnMr8abluh+OpcwPei8eYg1U2tvLh9lre2VrNqt11VPva2NfcQZdfEdnK5NwUvnPGeM6bNpRxOcluh3vMvJKgbGr4flTTogAMTx/kciTGmCNpbO1gy14fy3fUsn5PA6tL69hZ0wzA4Lho5owewsyRaWQMjqe9Zjff+sJ8kiKkA75XfosDU8PjJKaFwOXuhhS5ttY5A8XmplqCMsZLVJWapnbW72mgaHMlS7fVsqmiAXW+UzI8bRBThqVw5dx8CkelM214KjHRHw8IVFRUHjHJCTySoFS1U0T2Tw0fDTxgU8OHTkWTn9hoITMpzu1QjBnwmts7WbqthmU79vHKugq2VTcBzlQ4hflD+M4Z45kyLIWpw1MZmupuv6T+5okEBTY1fH9q6lAK84fYKBIRTETGALcCqap6idvxGIeqUtnYxqrddbxbXM27xdVsr27CrxATJUzLS+UHF0xmVEYiJxVkeqY/kls8k6BM//G1K2MTrZNuJAt02bhKRJ52O5aByO9XtlU3sa3Kx7qyerZW+the3cSu2maa252ZBOJiopiRl8r503KZMzqDwlHpAz4hHcwS1ABU16ZkJ8e7HYYxYU9Vqfa1sXWvj62Vjawva2BjRQPFlb4DiSg6SsgfksjozMHMG5vB6MzBjMtOZnZ+OnExNqHE4ViCGmCa2jpp7oScAVaXbUxfVfva2FLRyKaKRrbsbWRjRSNbyptpeeX1A9ukJ8YyZVgqlx0/gkm5KYzPSWZM1mBSEqzG4lhYghpgtgduwOZagopoIpIB/AyYKSK3qOovDrGNdXo/SKdf2dus1LT4KW9S9jb7Kff5KfX5aWz/eLukWBiRHMXxWcqItHhyBwvDk6JIixdEWoAW8FVR54OVJSEJ9ZAi7W9lCWqA+fvSnQAU5g9xORITSqpaA1x7hG0GbKf3ji4/W/Y2smq306doW5WPkqomdtW20OXXA9slJ8QwJiuF88YkM35oMhNykhk/NImspHhEJCLPTTD1NR5LUANMSZWPCelRrs3vYkx/8/uV7TVNFFf62FTeyMpd+3h/Ww3tnU5/wLiYKEZnDGZSbjLnT8tlbPZg8tITGZUxmMykOGvt6iJLUANMTVM7GfFW4CKdiOihlqtqxP/x2zq7WLFzH++X1PBucTXr9jQcSEYiMDpzMJfPGcnMkWnMGpnO8LRBREVF/GkJS5agBpjqxjZGZ1thjHQDIRGB04puR00zy7bX8sr6CrZW+ijd14xfIUpgel4a/3FCPuNykpg4NIWx2UkRNdJCpLO/1ACyu7aZhtZOsgbZCBKRTkQ+B5wPpAD3q+qrLofUZ6pKla+NzbVdFL+9jeU79rF8Zy3VPqf1Qm5qAoWjhvC544YxZXgqc0cPIS3RPuvhzFMJKhILlZcUV/oAGJVqfS8inao+BzwnIunAr4GwK0t1ze28tbWa9WX1bChvYGN5w4FkBBvJz0jkM+OzKMwfwvGj0hmblWRVdREmKAlKRB4ALgAqVXVqt+VHNY17JBQqL2vpcDoOJsVaIR5Avo8zW7Wnba9u4o2Ne6lpaqd0XwvbqnxsKHcGSY2LjmJcThKnTchmUm4KzRUlXHr2yWSnWFeJSBesK6gHgd8DD+9fcLhp3EVkGnBwv4yvqWpl4HlYFKpwc2CIFRtNJeKJ0/TsDuAlVV3pdjwH8/uVDeUNLF69h5fXVbCr1pk+IiZKyE1LYExmEjecVsC8sRkU5g/5xIgLRUU7LTkNEEFJUKr6loiMOmhxj9O4q+panCuuT/B6oQp3+6+gLEENCDcCZwKpIlKgqve6HRDA5opGnlq+mxfWlFPR0ArAvDEZfOXEUZwxKZuRQxKtWbc5IJT3oHo9jXs3RyxU1vv92K0ucervu1qbXY+lOy+cm+68Fs+xUNW7gbvdjmO/HdVNPPLBTv789nZE4LQJ2XxvwQROHJs54KaQML3XqwQlIq8DQw+x6lZVfT5YwfSmUA3k3u999c+q1WQMriQjNdb1WLrzwrnpzmvxhKvOLj93/6uY1zfsZUN5AwDzJ2Rxy7mTmDA0fKchN/2nVwlKVc88hn3bNO4es7asnml5qUCz26GYCKaqLF69hz8WlbCpopHZ+el89+zxnD99GKMzB7sdngkjoazis2ncPWZvQyuz8tOxBGVC5V+b9vKrV7awsbyB/IxE7vzCdL5YmGf3lcwxCVYz88eA+UCmiJQCt6vq/TaNu3e8sGYP+5o7mDQ0Gdpq3A7HRJj2Tj+/fnUzD7yzndRBsfzkoiksnDOS2Gjrc2eOXbBa8S3qYblN4+4BTW2d3Pb8eiYOTebyufm8/dYOt0MyEaS908+tz67lqRWlnDExm98uPM7mPzJB4amRJExo/OmtbdQ2tXPnF6YTbT3tTRDtrm3mm4+uZE1pPQuPH8EdX5judkgmgliCinBFmyt54J3tzBiRxukTs90Ox3iMiFwIXFhQUHDU7/1wey3X/X0FTe2d/OqS6XyxcMSR32TMUbAK4gjW1NbJN/62gqGpCfx+0Uwbp8x8iqouUdVrUlNTj/q997xZTHuXn39cd5IlJxMSlqAi2F1vbKWt08/PL55mExSaoCvd18wJYzKYPCzF7VBMhLIEFYG6/MrD7+/g/ne2k5uawJzRNr27Ca5Vu+soqWpidn6626GYCGb3oCJMeX0LNz2xmve31TAqI5G/XXWk0aWM6b0uv/LXd7dz1xtbSUuMZeHxVrVnQscSVIRQVZ79qIzvPrUaEeFnF0/l8zPzGGQjw5og6fIr33xkJS+vr+Dkgkx+cMFkmxDQhJQlqAjwXkk1P39xI+vKGpg6PIVfXDw9MKSRMcHz61c38/L6Cm48vYCbzhpvo0OYkPNUghKRMcCtQKqqXuJ2PF63o7qJO17axMvrK8hLH8RPPjeVywpHfGLuHGOCYWdNE38sKuHkgkz+6+wJbodjBgivzai7DbhKRJ4ORlyRSFX5YHstz6wo5blVZXR0KV85cRTfPWcCSfGe+r5hIshjH+4mSuD2Cye7HYoZQLw6o645hNJ9zdz2/Hr+tamShNgoFh4/kms+M8aakJuQ27K3kfE5yYzLsWkyTP/x1Iy65tAaWzv4Y1EJ9/67hJjoKL63YAJXzM0ndZCNd2b6x9bKRo4bYU3KTf/y1Iy6IpIB/AyYKSK3qOrBV1kDZkbdDr+yqrKLFXs7Wbm3i3Y/zMyO5spJcWRQykcflPZbLKFm8XhbTYuf3bUtXGajRZh+5rUZdWuAa4+wTUTPqLthTwMPvbeDZ1eV0d7pJz0xlkvnDGfBlFzmjc0IymCv4Xpu+ovX4nHbtno/AJ8Zn+VyJGagsRl1XVbX3M7SbbWUVPl4bcNeVu2uIy4miouPG86504ZyyrgsG4HcuKqmRQHIH2Kz4Zr+ZTPquqChtYP3iqtZsqacl9aW43fKPwXZSfzggsl8YdZw6wBp+kREPgecD6QA96vqq8e6r7o2P/ExUaQMslaipn/ZjLoh1tLexbZqHx9WdPLOCxv4YHstG8ob6PIriXHRXFo4gs/PymPq8BQS4+wfgAlOtw1VfQ54TkTSgV8DfUhQSnZKvHXMNf3OZtQNAr9fqW1up6K+lbK6FpZtr2VLpY+SSh9ldS0HtouL2cmskWlcd+pYPjM+i+NGpFmnWnMoD9LLbhs4yepwXTa+H3jfMatrU3KSE/qyC2OOiX1lP0qtHV0UV/p4v6SGjRUNfLSrjrJ9LbR3+Q9sExcdxfihSRSOSueyrBGMzUqiescGLjt3PgmxNjaeObyj6bYRaOn6qS4b4lzu3AG8pKor+xJPZbMyZnh8X3ZhzDGxBHUEO2uaeGNjJWtK61i3p4GSKh8auGeUnRzP9Lw0zp6SQ25KArlpg8hNTWB05mCSEz7ZR6modrMlJ9MXR9tt40bgTCBVRApU9d6DN+hNl419rX5qW5XW+mrPNL33UjcAL8UCkRePJSic4YPK61vZXNHIlr2NrCmtZ0N5A5UNrTS1dwFOMpqUm8L503IZm53E3NFDyEmxag/jTap6N3D3EbY5YpeNNzdVAsv46lmzOGWcN5qZe6kbgJdigciLZ8AlKL9f2VLZyMbyBjaWf/yz2td2YJuclHgKRw3htAnZjM4azKyRaUzOTbGbxMZNrnTbWFdWD2ATExpXRHSCqm/poLjSR3FlI9urm9lT18KKnfsONFyIi45iXE4S8ydkMW14KhOHJjNhaLI18TZe5Eq3jYbWDuKisRamxhUR+am7+42t3P9WM/Uvf9yyNjZayE0dxIghg7jihJGcOSmH0ZmDiY22VnTGW7zUbaOhpZPEGKs5MO6IyAQ1NCWB6Jf96QAABLVJREFUaZnRnDK9gILsJAqyk8hLT7QRGUxY8FK3jcnDUqiutMY9xh0RmaAuPX4E2U0lzD91rNuhGBPWvnziKPLbd7gdhhmgrH7LGGOMJ1mCMsYY40mWoIwxxniS6P5hEcKQiFQBO3tYnQlU92M4R+KleLwUC4RPPPmq6o3eqkFmZemYeSkWCJ94elWWwjpBHY6ILFfVQrfj2M9L8XgpFrB4vM5r58NL8XgpFoi8eKyKzxhjjCdZgjLGGONJkZyg7nM7gIN4KR4vxQIWj9d57Xx4KR4vxQIRFk/E3oMyxhgT3iL5CsoYY0wYi8gEJSILRGSziBSLyM39cLwRIvKmiGwQkfUi8u3A8iEi8pqIbA38TA8sFxG5OxDfGhGZFYKYokXkIxF5IfB6tIh8EDjmEyISF1geH3hdHFg/KgSxpInI0yKySUQ2isg8l8/Nfwb+TutE5DERSXDz/HiZlSUrS0eIJ7RlSVUj6oEz0nMJMAaIA1YDk0N8zFxgVuB5MrAFmAzcCdwcWH4z8MvA8/OAlwABTgA+CEFMNwGPAi8EXj8JLAw8vxe4LvD8euDewPOFwBMhiOUh4OuB53FAmlvnBmdm2u3AoG7n5Stunh+vPqwsHYjJytKhYwl5WXK9EITgDzgPeKXb61uAW/o5huf/f3vn7xpFFMTxz8BpxAjRWISTCBoQWyMWES3EHygi2qQRwfwLVoJY2YtoISIoFiIIapCQ0mgdNSAqajSgxITERMEIVopj8ebOIyQx4O3tcn4/8GDfvAdvdm6/zO7s4xY4AIwC5bCVgdE4vgocr5lfnVen9TuBIWAvMBgX6GegND9GpM837IzjUsyzOvrSFhexzbPnFZvKp9Pb43wHgYN5xafITVqSlv7iT+ZaasYSXyVoFSbC1hDisbUbGAY63H0qhqaBjjjO2seLwGngV/TXA1/d/ecC61V9ifG5mF8vNgOzwI0ok1wzs1Zyio27TwLngXFginS+I+QXnyIjLUlLi9IILTVjgsoNM1sD3ANOufu32jFPtw2Zb5k0syPAjLuPZL3WMikB24Er7t4NfCeVIao0KjYAUZ8/RhL7BqAVONSItcXykZYW5L/TUjMmqElgY02/M2yZYmYrSIK65e79Yf5kZuUYLwMzDfBxF3DUzD4At0mliUvAWjOrfP+rdr2qLzHeBnypky+Q7qAm3H04+ndJIssjNgD7gffuPuvuP4B+Uszyik+RkZakpaXIXEvNmKCeAFtiJ8lK0su4gSwXNDMDrgOv3f1CzdAA0BfHfaR6esV+MnbZ9ABzNY/o/4S7n3H3TnffRDr3h+5+AngE9C7iS8XH3phftzswd58GPprZ1jDtA16RQ2yCcaDHzFbH71bxJ5f4FBxpSVpaiuy1VK8XZkVqpN0rb0k7kM42YL3dpMfq58CzaIdJ9dUh4B3wAGiP+QZcDv9eADsy8msPf3YedQGPgTHgDtAS9lXRH4vxrgz82AY8jfjcB9blGRvgHPAGeAncBFryjE+Rm7RU9UtaWtifTLWkf5IQQghRSJqxxCeEEKIJUIISQghRSJSghBBCFBIlKCGEEIVECUoIIUQhUYISQghRSJSghBBCFBIlKCGEEIXkN83b1QnNKkiIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import NullFormatter  # useful for `logit` scale\n",
    "\n",
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "# make up some data in the open interval (0, 1)\n",
    "y = np.random.normal(loc=0.5, scale=0.4, size=1000)\n",
    "y = y[(y > 0) & (y < 1)]\n",
    "y.sort()\n",
    "x = np.arange(len(y))\n",
    "\n",
    "# plot with various axes scales\n",
    "plt.figure()\n",
    "\n",
    "# linear\n",
    "plt.subplot(221)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('linear')\n",
    "plt.title('linear')\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "# log\n",
    "plt.subplot(222)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('log')\n",
    "plt.title('log')\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "# symmetric log\n",
    "plt.subplot(223)\n",
    "plt.plot(x, y - y.mean())\n",
    "plt.yscale('symlog', linthreshy=0.01)\n",
    "plt.title('symlog')\n",
    "plt.grid(True)\n",
    "\n",
    "# logit\n",
    "plt.subplot(224)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('logit')\n",
    "plt.title('logit')\n",
    "plt.grid(True)\n",
    "# Adjust the subplot layout, because the logit one may take more space\n",
    "# than usual, due to y-tick labels like \"1 - 10^{-3}\"\n",
    "plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25, wspace=0.35)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
